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In this paper, we present a novel point-based digitally reconstructed radiography (PBDRR) method for large CT data sets. Three steps are mainly included in the proposed method, namely point sampling, point rendering and quantization. Finite samples are determined on the nature or user-specified transfer function by a Monte Carlo technique. These samples are projected onto an image plane and the intensity...
Computed tomographic colonography (CTC) provides a feasible way for the detection of colorectal polyps and cancer screening. In the clinical practice of CTC, a true colonic polyp will be confirmed with high confidence if a radiologist can find it in both the supine and prone scans. To assist radiologists in CTC reading, we propose a new colonic polyp matching method based on statistical curvature...
Our long term research goal is to develop a fully automated, image-based diagnostic system for early diagnosis of pulmonary nodules that may lead to lung cancer. This paper focuses on monitoring the development of lung nodules detected in successive chest low dose (LD) CT scans of a patient. We propose a new methodology for 3D LDCT data registration which is non-rigid and involves two steps: (i) global...
This paper presents a novel method for medical image signal fusion using complex-valued wavelets to enhance the information content in the fused signal from a perceptual manner. The proposed method introduces an adaptively weighted aggregation of signal characteristics based on the phase characteristics of medical image signals. The proposed method exploits the phase characteristics of the image signals...
An iterative classification method developed for 2D CT head data classification problem and using both statistical and spatial information is introduced in this paper. The method reduces the chance of misclassification, preserving the contiguity of tissue classes. This method is minimally supervised so that it enforces a relation between tissues and classes. In later iterations high-confidence points...
Computer-aided diagnostic (CAD) schemes often employ a filter for enhancement of lesions as a preprocessing step for improving sensitivity and specificity. The filter enhances objects similar to a model employed in the filter; e.g., a blob enhancement filter based on the Hessian matrix enhances sphere-like objects. Actual lesions, however, often differ from a simple model, e.g., a lung nodule is generally...
We present an algorithm for the segmentation of the liver in 2-D computed tomography slice images. The basis for our algorithm is an implicit active shape model. In order to detect the liver boundary and guide the shape model deformation, a boundary classifier has been integrated into the implicit framework in a novel manner. The accuracy of the algorithm has been evaluated for 20 test cases including...
Colon cancer is the second leading cause of cancer-related deaths per year in industrial nations. Virtual colonoscopy is a new, less invasive alternative to the usually practiced optical colonoscopy for colorectal polyp and cancer screening. In this paper, we present some physics-based modeling and pattern recognition techniques to identify anatomical landmarks in the human colon like the haustral...
An integrated and effective metal artifact reduction method named Metal Erasing (ME) especially suited to dental applications is proposed. Layout of metals is identified as metal-only tomogram, using its characteristics of X-ray opacity and simple image processing technique of binarization together with backward projection. Metal-only sinogram is calculated by forward projection of the metal-only...
CT colonography (CTC) is a rapidly evolving technique to screen for colorectal polyps. Fecal residue may occlude or, reversely, mimic polyps. Electronic cleansing aims at removing contrast-enhanced fecal residue from the image. However, thin layers of soft tissue (the colon wall or a fold) or residue are easily misclassified by current electronic cleansing methods, thereby causing holes in the colon...
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